apogee_rosemount_summer_2017
Erin Gilbert
08 May, 2018
SDS Rosemount
This RMarkdown generated principle components for data collected on the Apogee handheld
Planting notes: SDS Inoculated at planting Substrate in seed packets
Experimental Setup
Plot Experimental Design
#setwd("/Volumes/CFANS/")
directory<-"/Volumes//hirsch_lab/projects/soybean/sds_bsr/apogee/a8/raw"
files<-list.files(directory) #get all names in file
###Experiment info
experiment_info<-t(data.frame(strsplit(files, split="-")))
row.names(experiment_info)=NULL
#experiment_info<-experiment_info[,-ncol(experiment_info)]
colnames(experiment_info)<-unlist(strsplit(experiment_info[1,], split="_"))[seq(1,ncol(experiment_info)*2,2)]
#experiment_info<-experiment_info[,-ncol(experiment_info)]
experiment_info<-as.data.frame(apply(experiment_info, 2, function(x) unlist(strsplit(x,split="_")))[seq(2,nrow(experiment_info)*2,2),])
#experiment_info<-experiment_info[-1,]
# #Combining data
# apogee_transmission_readings<-read.table(paste(directory, files[1], sep=""), header = F, skip=3)
#
# for(i in 2:length(files))
# {
# temp<-read.table(paste(directory,files[i], sep=""), header=F, skip=3)
# apogee_transmission_readings<-cbind(apogee_transmission_readings, temp[,2])
#
# }
#
# names(apogee_transmission_readings)<-c("Wavelength", paste(experiment_info$Plot, experiment_info$Leaf, sep="_"))
#
#
#
# #clean up data
# apogee_transmission_readings<-subset(apogee_transmission_readings, Wavelength>400 & Wavelength<1200)
# #apogee_transmission_readings<-apogee_transmission_readings[,-unique(which(apogee_transmission_readings>150, arr.ind = T)[,2])[-1]]
# apogee_transmission_readings<-t(apogee_transmission_readings)[-1,]
apogee_transmission_readings<-read.csv("~/Desktop/apogee.csv")
apogee_transmission_readings_backup<-apogee_transmission_readings
#experiment_info<-experiment_info[which(experiment_info$Plot %in% as.data.frame(strsplit(paste(experiment_info$Plot, experiment_info$Leaf, sep="_"), split="_"), stringsAsFactors=F)[1,]),]
experiment_info$Leaf<-gsub('.TRM|1|2|3','',experiment_info$Leaf)
experiment_info_backup<-experiment_info
#experiment_info["means"]<-colMeans(apogee_transmission_readings[,-1])
unik<-!duplicated(experiment_info$Plot)
experiment_info_reduced<-experiment_info[unik,]
experiment_info_reduced<-experiment_info_reduced[,1:6]
datatable(experiment_info_reduced)Readings Log
datatable(experiment_info)Principle components on leaves
PCs All leaves by inoculation status
PLOTS SHOW PC1xPC2 and PC3&PC4 FOR EACH DAY
Uninoculated = Blue | Inoculated= Orange
MINNSOY
apogee_transmission_readings<-apogee_transmission_readings_backup
genotype=unique(experiment_info$Genotype)[1] ##change the number for the genotype you want
#unique(experiment_info$Genotype) = Minnsoy Noir McCall MN1606 Ag2107 MN1410
for (plot_day in sort(as.numeric(as.character(unique(experiment_info$PictureDay)))))
{
apogee_transmission_readings_plot<-as.data.frame(apogee_transmission_readings[which(experiment_info$PictureDay==plot_day & experiment_info$Genotype == genotype),])
if (nrow(apogee_transmission_readings_plot)>1){ #if there is more than 1 reading
# curve_areas_means<-NULL
# for(i in 1:ncol(apogee_transmission_readings_means_plot))
# {
# curve_areas_means<-cbind(curve_areas_means, diff(apogee_transmission_readings_means_plot[,i])/diff(apogee_transmission_readings_means_plot_day[,1]))
# }
pca_plot_mean<-prcomp(apogee_transmission_readings_plot)
#par(mfrow=c(4,1))
# plots_for_plot_day<-sort(unique(unlist(strsplit(names(apogee_transmission_readings_means_plot_day)[grep(paste("_", plot_day, sep=""),names(apogee_transmission_readings_means_plot_day))], split = "_"))))[-1]
clr<-c("blue", "orange")
if(ncol(pca_plot_mean$x) >1) #if there was enough data to generate at least 2 PCs
{plot(pca_plot_mean$x[,1], pca_plot_mean$x[,2], col=clr[as.numeric(experiment_info$Treatment[which(experiment_info$PictureDay==plot_day & experiment_info$Genotype ==genotype)])], main=paste("PC 1&2", genotype,"All Leaves per Plot Day", plot_day), xlab="PC1", ylab="PC2", pch=20)}
if(ncol(pca_plot_mean$x) >3) #if PC 3 and 4 are there
{plot(pca_plot_mean$x[,3], pca_plot_mean$x[,4], col=clr[as.numeric(experiment_info$Treatment[which(experiment_info$PictureDay==plot_day & experiment_info$Genotype ==genotype)])], main=paste("PC 3&4", genotype,"All Leaves per Plot Day", plot_day), xlab="PC3", ylab="PC4", pch=20)}
#legend("bottomleft",legend=c("uninoc", "inoc"), col=c("orange", "blue"), pch=19)#colorbyinoculationstatus
}
}
NOIR
apogee_transmission_readings<-apogee_transmission_readings_backup
genotype=unique(experiment_info$Genotype)[2] ##change the number for the genotype you want
#unique(experiment_info$Genotype) = Minnsoy Noir McCall MN1606 Ag2107 MN1410
for (plot_day in sort(as.numeric(as.character(unique(experiment_info$PictureDay)))))
{
apogee_transmission_readings_plot<-as.data.frame(apogee_transmission_readings[which(experiment_info$PictureDay==plot_day & experiment_info$Genotype == genotype),])
if (nrow(apogee_transmission_readings_plot)>1){ #if there is more than 1 reading
# curve_areas_means<-NULL
# for(i in 1:ncol(apogee_transmission_readings_means_plot))
# {
# curve_areas_means<-cbind(curve_areas_means, diff(apogee_transmission_readings_means_plot[,i])/diff(apogee_transmission_readings_means_plot_day[,1]))
# }
pca_plot_mean<-prcomp(apogee_transmission_readings_plot)
#par(mfrow=c(4,1))
# plots_for_plot_day<-sort(unique(unlist(strsplit(names(apogee_transmission_readings_means_plot_day)[grep(paste("_", plot_day, sep=""),names(apogee_transmission_readings_means_plot_day))], split = "_"))))[-1]
clr<-c("blue", "orange")
if(ncol(pca_plot_mean$x) >1) #if there was enough data to generate at least 2 PCs
{plot(pca_plot_mean$x[,1], pca_plot_mean$x[,2], col=clr[as.numeric(experiment_info$Treatment[which(experiment_info$PictureDay==plot_day & experiment_info$Genotype ==genotype)])], main=paste("PC 1&2", genotype,"All Leaves per Plot Day", plot_day), xlab="PC1", ylab="PC2", pch=20)}
if(ncol(pca_plot_mean$x) >3) #if PC 3 and 4 are there
{plot(pca_plot_mean$x[,3], pca_plot_mean$x[,4], col=clr[as.numeric(experiment_info$Treatment[which(experiment_info$PictureDay==plot_day & experiment_info$Genotype ==genotype)])], main=paste("PC 3&4", genotype,"All Leaves per Plot Day", plot_day), xlab="PC3", ylab="PC4", pch=20)}
#legend("bottomleft",legend=c("uninoc", "inoc"), col=c("orange", "blue"), pch=19)#colorbyinoculationstatus
}
}
clr<-c(“blue”, “orange”) if(ncol(pca_plot_mean\(x) >1) #if there was enough data to generate at least 2 PCs {plot(pca_plot_mean\)x[,1], pca_plot_mean\(x[,2], col=clr[as.numeric(experiment_info\)Treatment[which(experiment_info\(PictureDay==plot_day & experiment_info\)Genotype ==genotype)])], main=paste(“PC 1&2”, genotype,“All Leaves per Plot Day”, plot_day), xlab=“PC1”, ylab=“PC2”, pch=20)} if(ncol(pca_plot_mean\(x) >3) #if PC 3 and 4 are there {plot(pca_plot_mean\)x[,3], pca_plot_mean\(x[,4], col=clr[as.numeric(experiment_info\)Treatment[which(experiment_info\(PictureDay==plot_day & experiment_info\)Genotype ==genotype)])], main=paste(“PC 3&4”, genotype,“All Leaves per Plot Day”, plot_day), xlab=“PC3”, ylab=“PC4”, pch=20)} #legend(“bottomleft”,legend=c(“uninoc”, “inoc”), col=c(“orange”, “blue”), pch=19)#colorbyinoculationstatus } } ```
MN1606
apogee_transmission_readings<-apogee_transmission_readings_backup
genotype=unique(experiment_info$Genotype)[4] ##change the number for the genotype you want
#unique(experiment_info$Genotype) = Minnsoy Noir McCall MN1606 Ag2107 MN1410
for (plot_day in sort(as.numeric(as.character(unique(experiment_info$PictureDay)))))
{
apogee_transmission_readings_plot<-as.data.frame(apogee_transmission_readings[which(experiment_info$PictureDay==plot_day & experiment_info$Genotype == genotype),])
if (nrow(apogee_transmission_readings_plot)>1){ #if there is more than 1 reading
# curve_areas_means<-NULL
# for(i in 1:ncol(apogee_transmission_readings_means_plot))
# {
# curve_areas_means<-cbind(curve_areas_means, diff(apogee_transmission_readings_means_plot[,i])/diff(apogee_transmission_readings_means_plot_day[,1]))
# }
pca_plot_mean<-prcomp(apogee_transmission_readings_plot)
#par(mfrow=c(4,1))
# plots_for_plot_day<-sort(unique(unlist(strsplit(names(apogee_transmission_readings_means_plot_day)[grep(paste("_", plot_day, sep=""),names(apogee_transmission_readings_means_plot_day))], split = "_"))))[-1]
clr<-c("blue", "orange")
if(ncol(pca_plot_mean$x) >1) #if there was enough data to generate at least 2 PCs
{plot(pca_plot_mean$x[,1], pca_plot_mean$x[,2], col=clr[as.numeric(experiment_info$Treatment[which(experiment_info$PictureDay==plot_day & experiment_info$Genotype ==genotype)])], main=paste("PC 1&2", genotype,"All Leaves per Plot Day", plot_day), xlab="PC1", ylab="PC2", pch=20)}
if(ncol(pca_plot_mean$x) >3) #if PC 3 and 4 are there
{plot(pca_plot_mean$x[,3], pca_plot_mean$x[,4], col=clr[as.numeric(experiment_info$Treatment[which(experiment_info$PictureDay==plot_day & experiment_info$Genotype ==genotype)])], main=paste("PC 3&4", genotype,"All Leaves per Plot Day", plot_day), xlab="PC3", ylab="PC4", pch=20)}
#legend("bottomleft",legend=c("uninoc", "inoc"), col=c("orange", "blue"), pch=19)#colorbyinoculationstatus
}
}
AG2107
apogee_transmission_readings<-apogee_transmission_readings_backup
genotype=unique(experiment_info$Genotype)[5] ##change the number for the genotype you want
#unique(experiment_info$Genotype) = Minnsoy Noir McCall MN1606 Ag2107 MN1410
for (plot_day in sort(as.numeric(as.character(unique(experiment_info$PictureDay)))))
{
apogee_transmission_readings_plot<-as.data.frame(apogee_transmission_readings[which(experiment_info$PictureDay==plot_day & experiment_info$Genotype == genotype),])
if (nrow(apogee_transmission_readings_plot)>1){ #if there is more than 1 reading
# curve_areas_means<-NULL
# for(i in 1:ncol(apogee_transmission_readings_means_plot))
# {
# curve_areas_means<-cbind(curve_areas_means, diff(apogee_transmission_readings_means_plot[,i])/diff(apogee_transmission_readings_means_plot_day[,1]))
# }
pca_plot_mean<-prcomp(apogee_transmission_readings_plot)
#par(mfrow=c(4,1))
# plots_for_plot_day<-sort(unique(unlist(strsplit(names(apogee_transmission_readings_means_plot_day)[grep(paste("_", plot_day, sep=""),names(apogee_transmission_readings_means_plot_day))], split = "_"))))[-1]
clr<-c("blue", "orange")
if(ncol(pca_plot_mean$x) >1) #if there was enough data to generate at least 2 PCs
{plot(pca_plot_mean$x[,1], pca_plot_mean$x[,2], col=clr[as.numeric(experiment_info$Treatment[which(experiment_info$PictureDay==plot_day & experiment_info$Genotype ==genotype)])], main=paste("PC 1&2", genotype,"All Leaves per Plot Day", plot_day), xlab="PC1", ylab="PC2", pch=20)}
if(ncol(pca_plot_mean$x) >3) #if PC 3 and 4 are there
{plot(pca_plot_mean$x[,3], pca_plot_mean$x[,4], col=clr[as.numeric(experiment_info$Treatment[which(experiment_info$PictureDay==plot_day & experiment_info$Genotype ==genotype)])], main=paste("PC 3&4", genotype,"All Leaves per Plot Day", plot_day), xlab="PC3", ylab="PC4", pch=20)}
#legend("bottomleft",legend=c("uninoc", "inoc"), col=c("orange", "blue"), pch=19)#colorbyinoculationstatus
}
}
MN1410
apogee_transmission_readings<-apogee_transmission_readings_backup
genotype=unique(experiment_info$Genotype)[6] ##change the number for the genotype you want
#unique(experiment_info$Genotype) = Minnsoy Noir McCall MN1606 Ag2107 MN1410
for (plot_day in sort(as.numeric(as.character(unique(experiment_info$PictureDay)))))
{
apogee_transmission_readings_plot<-as.data.frame(apogee_transmission_readings[which(experiment_info$PictureDay==plot_day & experiment_info$Genotype == genotype),])
if (nrow(apogee_transmission_readings_plot)>1){ #if there is more than 1 reading
# curve_areas_means<-NULL
# for(i in 1:ncol(apogee_transmission_readings_means_plot))
# {
# curve_areas_means<-cbind(curve_areas_means, diff(apogee_transmission_readings_means_plot[,i])/diff(apogee_transmission_readings_means_plot_day[,1]))
# }
pca_plot_mean<-prcomp(apogee_transmission_readings_plot)
#par(mfrow=c(4,1))
# plots_for_plot_day<-sort(unique(unlist(strsplit(names(apogee_transmission_readings_means_plot_day)[grep(paste("_", plot_day, sep=""),names(apogee_transmission_readings_means_plot_day))], split = "_"))))[-1]
clr<-c("blue", "orange")
if(ncol(pca_plot_mean$x) >1) #if there was enough data to generate at least 2 PCs
{plot(pca_plot_mean$x[,1], pca_plot_mean$x[,2], col=clr[as.numeric(experiment_info$Treatment[which(experiment_info$PictureDay==plot_day & experiment_info$Genotype ==genotype)])], main=paste("PC 1&2", genotype,"All Leaves per Plot Day", plot_day), xlab="PC1", ylab="PC2", pch=20)}
if(ncol(pca_plot_mean$x) >3) #if PC 3 and 4 are there
{plot(pca_plot_mean$x[,3], pca_plot_mean$x[,4], col=clr[as.numeric(experiment_info$Treatment[which(experiment_info$PictureDay==plot_day & experiment_info$Genotype ==genotype)])], main=paste("PC 3&4", genotype,"All Leaves per Plot Day", plot_day), xlab="PC3", ylab="PC4", pch=20)}
#legend("bottomleft",legend=c("uninoc", "inoc"), col=c("orange", "blue"), pch=19)#colorbyinoculationstatus
}
}
PCs Midleaf by inoculation status
PLOTS SHOW PC1xPC2 and PC3&PC4 FOR EACH DAY
Uninoculated = Blue | Inoculated= Orange
MINNSOY
apogee_transmission_readings<-apogee_transmission_readings_backup[which(experiment_info_backup$Leaf=="C"),]
genotype=unique(experiment_info$Genotype)[1] ##change the number for the genotype you want
#unique(experiment_info$Genotype) = Minnsoy Noir McCall MN1606 Ag2107 MN1410
experiment_info<-experiment_info_backup[which(experiment_info$Leaf=="C"),]
for (plot_day in sort(as.numeric(as.character(unique(experiment_info$PictureDay)))))
{
apogee_transmission_readings_plot<-as.data.frame(apogee_transmission_readings[which(experiment_info$PictureDay==plot_day & experiment_info$Genotype == genotype),])
if (nrow(apogee_transmission_readings_plot)>1){
# curve_areas_means<-NULL
# for(i in 1:ncol(apogee_transmission_readings_means_plot))
# {
# curve_areas_means<-cbind(curve_areas_means, diff(apogee_transmission_readings_means_plot[,i])/diff(apogee_transmission_readings_means_plot_day[,1]))
# }
pca_plot_mean<-prcomp(apogee_transmission_readings_plot)
#par(mfrow=c(4,1))
# plots_for_plot_day<-sort(unique(unlist(strsplit(names(apogee_transmission_readings_means_plot_day)[grep(paste("_", plot_day, sep=""),names(apogee_transmission_readings_means_plot_day))], split = "_"))))[-1]
clr<-c("blue", "orange")
if(ncol(pca_plot_mean$x) >1)
{plot(pca_plot_mean$x[,1], pca_plot_mean$x[,2], col=clr[as.numeric(experiment_info$Treatment[which(experiment_info$PictureDay==plot_day & experiment_info$Genotype ==genotype)])], main=paste("PC 1&2", genotype,"Midleaf per Plot Day", plot_day), xlab="PC1", ylab="PC2", pch=20)}
if(ncol(pca_plot_mean$x) >3)
{plot(pca_plot_mean$x[,3], pca_plot_mean$x[,4], col=clr[as.numeric(experiment_info$Treatment[which(experiment_info$PictureDay==plot_day & experiment_info$Genotype ==genotype)])], main=paste("PC 3&4", genotype,"Midleaf per Plot Day", plot_day), xlab="PC3", ylab="PC4", pch=20)}
#legend("bottomleft",legend=c("uninoc", "inoc"), col=c("orange", "blue"), pch=19)#colorbyinoculationstatus
}
}
experiment_info<-experiment_info_backupNoir
apogee_transmission_readings<-apogee_transmission_readings_backup[which(experiment_info$Leaf=="C"),]
genotype=unique(experiment_info$Genotype)[2] ##change the number for the genotype you want
#unique(experiment_info$Genotype) = Minnsoy Noir McCall MN1606 Ag2107 MN1410
experiment_info<-experiment_info_backup[which(experiment_info$Leaf=="C"),]
for (plot_day in sort(as.numeric(as.character(unique(experiment_info$PictureDay)))))
{
apogee_transmission_readings_plot<-as.data.frame(apogee_transmission_readings[which(experiment_info$PictureDay==plot_day & experiment_info$Genotype == genotype),])
if (nrow(apogee_transmission_readings_plot)>1){
# curve_areas_means<-NULL
# for(i in 1:ncol(apogee_transmission_readings_means_plot))
# {
# curve_areas_means<-cbind(curve_areas_means, diff(apogee_transmission_readings_means_plot[,i])/diff(apogee_transmission_readings_means_plot_day[,1]))
# }
pca_plot_mean<-prcomp(apogee_transmission_readings_plot)
#par(mfrow=c(4,1))
# plots_for_plot_day<-sort(unique(unlist(strsplit(names(apogee_transmission_readings_means_plot_day)[grep(paste("_", plot_day, sep=""),names(apogee_transmission_readings_means_plot_day))], split = "_"))))[-1]
clr<-c("blue", "orange")
if(ncol(pca_plot_mean$x) >1)
{plot(pca_plot_mean$x[,1], pca_plot_mean$x[,2], col=clr[as.numeric(experiment_info$Treatment[which(experiment_info$PictureDay==plot_day & experiment_info$Genotype ==genotype)])], main=paste("PC 1&2", genotype,"Midleaf per Plot Day", plot_day), xlab="PC1", ylab="PC2", pch=20)}
if(ncol(pca_plot_mean$x) >3)
{plot(pca_plot_mean$x[,3], pca_plot_mean$x[,4], col=clr[as.numeric(experiment_info$Treatment[which(experiment_info$PictureDay==plot_day & experiment_info$Genotype ==genotype)])], main=paste("PC 3&4", genotype,"Midleaf per Plot Day", plot_day), xlab="PC3", ylab="PC4", pch=20)}
#legend("bottomleft",legend=c("uninoc", "inoc"), col=c("orange", "blue"), pch=19)#colorbyinoculationstatus
}
}
experiment_info<-experiment_info_backupMCCALL
apogee_transmission_readings<-apogee_transmission_readings_backup[which(experiment_info$Leaf=="C"),]
genotype=unique(experiment_info$Genotype)[3] ##change the number for the genotype you want
#unique(experiment_info$Genotype) = Minnsoy Noir McCall MN1606 Ag2107 MN1410
experiment_info<-experiment_info_backup[which(experiment_info$Leaf=="C"),]
for (plot_day in sort(as.numeric(as.character(unique(experiment_info$PictureDay)))))
{
apogee_transmission_readings_plot<-as.data.frame(apogee_transmission_readings[which(experiment_info$PictureDay==plot_day & experiment_info$Genotype == genotype),])
if (nrow(apogee_transmission_readings_plot)>1){
# curve_areas_means<-NULL
# for(i in 1:ncol(apogee_transmission_readings_means_plot))
# {
# curve_areas_means<-cbind(curve_areas_means, diff(apogee_transmission_readings_means_plot[,i])/diff(apogee_transmission_readings_means_plot_day[,1]))
# }
pca_plot_mean<-prcomp(apogee_transmission_readings_plot)
#par(mfrow=c(4,1))
# plots_for_plot_day<-sort(unique(unlist(strsplit(names(apogee_transmission_readings_means_plot_day)[grep(paste("_", plot_day, sep=""),names(apogee_transmission_readings_means_plot_day))], split = "_"))))[-1]
clr<-c("blue", "orange")
if(ncol(pca_plot_mean$x) >1)
{plot(pca_plot_mean$x[,1], pca_plot_mean$x[,2], col=clr[as.numeric(experiment_info$Treatment[which(experiment_info$PictureDay==plot_day & experiment_info$Genotype ==genotype)])], main=paste("PC 1&2", genotype,"Midleaf per Plot Day", plot_day), xlab="PC1", ylab="PC2", pch=20)}
if(ncol(pca_plot_mean$x) >3)
{plot(pca_plot_mean$x[,3], pca_plot_mean$x[,4], col=clr[as.numeric(experiment_info$Treatment[which(experiment_info$PictureDay==plot_day & experiment_info$Genotype ==genotype)])], main=paste("PC 3&4", genotype,"Midleaf per Plot Day", plot_day), xlab="PC3", ylab="PC4", pch=20)}
#legend("bottomleft",legend=c("uninoc", "inoc"), col=c("orange", "blue"), pch=19)#colorbyinoculationstatus
}
}
experiment_info<-experiment_info_backupMN1606
apogee_transmission_readings<-apogee_transmission_readings_backup[which(experiment_info$Leaf=="C"),]
genotype=unique(experiment_info$Genotype)[4] ##change the number for the genotype you want
#unique(experiment_info$Genotype) = Minnsoy Noir McCall MN1606 Ag2107 MN1410
experiment_info<-experiment_info_backup[which(experiment_info$Leaf=="C"),]
for (plot_day in sort(as.numeric(as.character(unique(experiment_info$PictureDay)))))
{
apogee_transmission_readings_plot<-as.data.frame(apogee_transmission_readings[which(experiment_info$PictureDay==plot_day & experiment_info$Genotype == genotype),])
if (nrow(apogee_transmission_readings_plot)>1){
# curve_areas_means<-NULL
# for(i in 1:ncol(apogee_transmission_readings_means_plot))
# {
# curve_areas_means<-cbind(curve_areas_means, diff(apogee_transmission_readings_means_plot[,i])/diff(apogee_transmission_readings_means_plot_day[,1]))
# }
pca_plot_mean<-prcomp(apogee_transmission_readings_plot)
#par(mfrow=c(4,1))
# plots_for_plot_day<-sort(unique(unlist(strsplit(names(apogee_transmission_readings_means_plot_day)[grep(paste("_", plot_day, sep=""),names(apogee_transmission_readings_means_plot_day))], split = "_"))))[-1]
clr<-c("blue", "orange")
if(ncol(pca_plot_mean$x) >1)
{plot(pca_plot_mean$x[,1], pca_plot_mean$x[,2], col=clr[as.numeric(experiment_info$Treatment[which(experiment_info$PictureDay==plot_day & experiment_info$Genotype ==genotype)])], main=paste("PC 1&2", genotype,"Midleaf per Plot Day", plot_day), xlab="PC1", ylab="PC2", pch=20)}
if(ncol(pca_plot_mean$x) >3)
{plot(pca_plot_mean$x[,3], pca_plot_mean$x[,4], col=clr[as.numeric(experiment_info$Treatment[which(experiment_info$PictureDay==plot_day & experiment_info$Genotype ==genotype)])], main=paste("PC 3&4", genotype,"Midleaf per Plot Day", plot_day), xlab="PC3", ylab="PC4", pch=20)}
#legend("bottomleft",legend=c("uninoc", "inoc"), col=c("orange", "blue"), pch=19)#colorbyinoculationstatus
}
}
experiment_info<-experiment_info_backupAG2107
apogee_transmission_readings<-apogee_transmission_readings_backup[which(experiment_info$Leaf=="C"),]
genotype=unique(experiment_info$Genotype)[5] ##change the number for the genotype you want
#unique(experiment_info$Genotype) = Minnsoy Noir McCall MN1606 Ag2107 MN1410
experiment_info<-experiment_info_backup[which(experiment_info$Leaf=="C"),]
for (plot_day in sort(as.numeric(as.character(unique(experiment_info$PictureDay)))))
{
apogee_transmission_readings_plot<-as.data.frame(apogee_transmission_readings[which(experiment_info$PictureDay==plot_day & experiment_info$Genotype == genotype),])
if (nrow(apogee_transmission_readings_plot)>1){
# curve_areas_means<-NULL
# for(i in 1:ncol(apogee_transmission_readings_means_plot))
# {
# curve_areas_means<-cbind(curve_areas_means, diff(apogee_transmission_readings_means_plot[,i])/diff(apogee_transmission_readings_means_plot_day[,1]))
# }
pca_plot_mean<-prcomp(apogee_transmission_readings_plot)
#par(mfrow=c(4,1))
# plots_for_plot_day<-sort(unique(unlist(strsplit(names(apogee_transmission_readings_means_plot_day)[grep(paste("_", plot_day, sep=""),names(apogee_transmission_readings_means_plot_day))], split = "_"))))[-1]
clr<-c("blue", "orange")
if(ncol(pca_plot_mean$x) >1)
{plot(pca_plot_mean$x[,1], pca_plot_mean$x[,2], col=clr[as.numeric(experiment_info$Treatment[which(experiment_info$PictureDay==plot_day & experiment_info$Genotype ==genotype)])], main=paste("PC 1&2", genotype,"Midleaf per Plot Day", plot_day), xlab="PC1", ylab="PC2", pch=20)}
if(ncol(pca_plot_mean$x) >3)
{plot(pca_plot_mean$x[,3], pca_plot_mean$x[,4], col=clr[as.numeric(experiment_info$Treatment[which(experiment_info$PictureDay==plot_day & experiment_info$Genotype ==genotype)])], main=paste("PC 3&4", genotype,"Midleaf per Plot Day", plot_day), xlab="PC3", ylab="PC4", pch=20)}
#legend("bottomleft",legend=c("uninoc", "inoc"), col=c("orange", "blue"), pch=19)#colorbyinoculationstatus
}
}
experiment_info<-experiment_info_backupMN1410
apogee_transmission_readings<-apogee_transmission_readings_backup[which(experiment_info$Leaf=="C"),]
genotype=unique(experiment_info$Genotype)[6] ##change the number for the genotype you want
#unique(experiment_info$Genotype) = Minnsoy Noir McCall MN1606 Ag2107 MN1410
experiment_info<-experiment_info_backup[which(experiment_info$Leaf=="C"),]
for (plot_day in sort(as.numeric(as.character(unique(experiment_info$PictureDay)))))
{
apogee_transmission_readings_plot<-as.data.frame(apogee_transmission_readings[which(experiment_info$PictureDay==plot_day & experiment_info$Genotype == genotype),])
if (nrow(apogee_transmission_readings_plot)>1){
# curve_areas_means<-NULL
# for(i in 1:ncol(apogee_transmission_readings_means_plot))
# {
# curve_areas_means<-cbind(curve_areas_means, diff(apogee_transmission_readings_means_plot[,i])/diff(apogee_transmission_readings_means_plot_day[,1]))
# }
pca_plot_mean<-prcomp(apogee_transmission_readings_plot)
#par(mfrow=c(4,1))
# plots_for_plot_day<-sort(unique(unlist(strsplit(names(apogee_transmission_readings_means_plot_day)[grep(paste("_", plot_day, sep=""),names(apogee_transmission_readings_means_plot_day))], split = "_"))))[-1]
clr<-c("blue", "orange")
if(ncol(pca_plot_mean$x) >1)
{plot(pca_plot_mean$x[,1], pca_plot_mean$x[,2], col=clr[as.numeric(experiment_info$Treatment[which(experiment_info$PictureDay==plot_day & experiment_info$Genotype ==genotype)])], main=paste("PC 1&2", genotype,"Midleaf per Plot Day", plot_day), xlab="PC1", ylab="PC2", pch=20)}
if(ncol(pca_plot_mean$x) >3)
{plot(pca_plot_mean$x[,3], pca_plot_mean$x[,4], col=clr[as.numeric(experiment_info$Treatment[which(experiment_info$PictureDay==plot_day & experiment_info$Genotype ==genotype)])], main=paste("PC 3&4", genotype,"Midleaf per Plot Day", plot_day), xlab="PC3", ylab="PC4", pch=20)}
#legend("bottomleft",legend=c("uninoc", "inoc"), col=c("orange", "blue"), pch=19)#colorbyinoculationstatus
}
}
experiment_info<-experiment_info_backup